NOLOGIES TRADA ADAMUS CHNOLOGIES Robotic Arm Control Via EMG - PowerPoint PPT Presentation

N OSTR MUS T ECH NOLOGIES TRADA ADAMUS CHNOLOGIES Robotic Arm Control Via EMG Signal U NIVERSITY OF S OUTH C AROLINA Don Groves – Leader Kevin Tangen Jake Tomlinson grovesd@email.sc.edu tangenk@email.sc.edu Tomlins2@email.sc.edu

Problem Definition • Global need for specialized surgeons – Third world countries – Hostile environments • Obstacles: – Time – Money – Safety

Background • Current robotic systems: – Supervisory control system – Telesurgical system • Da Vinci surgical system – Shared control system

Components

Project Progression BioRadio AL5B • EMG Signals • Signal Filtering • Differential Signal Analysis • Programming • Signal Amplification • Muscles • LabVIEW Integration Languages • 3 Servo Motors • Data Collection • BioRadio Control • Robotic Arm Models • Practice Controlling • LabVIEW Integration Research LabVIEW Full System

LabVIEW Program

LabVIEW Control Panel

Results Develop a VI in LabVIEW to control all components Control a single servo motor using a live EMG Perform tests to Incorporate three assess accuracy EMG channels and precision

Data Pointing Accuracy Depth Test Trial Distance from Target (cm) Trial Target Depth (cm) Experimental (cm) Percent Error 1 3.00 1 2.00 4.50 125.00 2 1.20 2 2.00 3.70 85.00 3 0.40 3 2.00 0.70 65.00 4 2.30 4 2.00 2.60 30.00 5 0.80 5 2.00 3.10 55.00 6 0.60 6 2.00 1.60 20.00 7 1.10 7 2.00 2.10 5.00 8 0.50 8 2.00 2.40 20.00 9 0.20 9 2.00 1.80 10.00 10 0.80 10 2.00 1.90 5.00 Test Cut Length Trial Target length (cm) Experimental (cm) Percent Error 1 8.00 5.50 31.25 2 8.00 2.40 70.00 3 8.00 6.00 25.00 4 8.00 8.50 6.25 5 8.00 9.30 16.25 6 8.00 6.90 13.75 7 8.00 7.60 5.00 8 8.00 7.20 10.00 9 8.00 5.80 27.50 10 8.00 8.20 2.50

Wrist Control • Video

Elbow Joint • Video

Shoulder Control • Video

Robotic Arm Arc Cut • Video

Twitchy Cut • Video

Failed Cut • Video

Successful Cut • Video

Straight line • Video

Robotic Arm 2-Planes • Video

Robotic Arm Back and Forth • Video

Obstacles

Conclusion Obtained and processed EMG signal Isolated robotic arm to 3 DOF Operated 3 motors with 3 corresponding pairs of opposing muscle groups Performed cutting motion of specified length and depth using the BioRadio/LabVIEW/AL5B system

Acknowledgements • We would like to thank: – Our advisor – Dr. Abdel Bayoumi, USC – Our sponsor – Dr. Joseph Giuffrida, CleveMed – Our thesis second readers – Dr. Francisco Gonzalez, USC; Dr. Brian Helmuth, USC – Our technical support – Russell Tomlinson, Robert

References • Artemiadis, P.K., Kyriakopoulos , K.J., “EMG -based teleoperation of a robot arm in • planar catching movements using ARMAX model and trajectory monitoring techniques.” IEEE Transactions on Robotics and Automation (2006): 3244-49. Print. • • Bonsor , Kevin, “How Robotic Surgery Will Work.” Discovery Company . • http://science.howstuffworks.com/robotic-surgery4.htm accessed: 04/05/2011 • • Farry, K.A., Walker, I.D., and Baraniuk , R.G., “ Myoelectric teleoperation of a complex • robotic hand.” IEEE Transactions on Robotics and Automation, 12.5 (1996) . • • Han, J., Song, W., Kim, J., Bang, W., Lee, H., and Bien, Z., “New EMG Pattern • Recognition based on Soft Computing Techniques and Its Application to Control • of a Rehabilitation Robotic Arm.” KAIST (2000): 890-97. Print. • • Hidalgo, M., Tene, G., and Sánchez , A., “Fuzzy Control of a Robotic Arm using EMG • Signals.” IEEE, (2005). • • Kiguchi, K., Tanaka, T., and Fukuda, T., "Neuro-Fuzzy Control of a Robotic Exoskeleton • With EMG Signals." IEEE Transactions on Fuzzy Systems 12.4 (2004): 481-90. Print.